PyTorch实现高分遥感语义分割(地物分类)
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Updated
Nov 11, 2020 - Python
PyTorch实现高分遥感语义分割(地物分类)
Satellite image time series in R
A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.
Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the earth from June, 2015. This image patches can be trained and classified using transfer learning techniques.
Land Cover Classification System Database Model
Classification of land based on land cover data.
Land Cover Classification System Web Service
Land Cover Classification System Web Service Specification
Study about Urban Green Spaces in Athens GR, using the Google Earth Engine platform, along with Landsat 8 and 9 imagery and Random Forest supervised machine learning algorithms.
A deep learning (neural network) land cover classification project using satellite images (remote sensing).
Multi-scale patch-wise semantic segmentation of satellite images using U-Net architecture.
Photovoltaic Farms Mapping with openEO
LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery
Pipelines for BigEarthNet-Sen1 creation.
Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities
A repository to showcase environmental projects implemented with Google Earth Engine platform, Javascript and machine learning algorithms.
The source code of the Sentinel-2 Land Cover Explorer has been moved to https://github.com/Esri/imagery-explorer-apps
ASI-304: Applying AI and Machine Learning to Satellite Data
codes for RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery
Crop type mapping solution for MAGO Project (NTUA)
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